Prototype-Based Learning for Healthcare: A Demonstration of Interpretable AI
This addresses the need for interpretable AI in healthcare, but appears incremental as it applies an existing method to a specific domain.
The paper tackles the problem of making personalized preventive healthcare predictions and interventions understandable and verifiable for stakeholders, and demonstrates that their prototype-based learning framework, ProtoPal, achieves superior quantitative performance with intuitive presentations.
Despite recent advances in machine learning and explainable AI, a gap remains in personalized preventive healthcare: predictions, interventions, and recommendations should be both understandable and verifiable for all stakeholders in the healthcare sector. We present a demonstration of how prototype-based learning can address these needs. Our proposed framework, ProtoPal, features both front- and back-end modes; it achieves superior quantitative performance while also providing an intuitive presentation of interventions and their simulated outcomes.